Conventionally, in order to restore a blurred image having been caused, for example, when the imaging lens of an imaging device, such as a camera, has become out of focus, a method is used that extracts the edge components from the blurred image and adds the edge components to the original blurred image while changing the ratio to perform edge enhancement, thereby making the image clear.
Further, in recent years, methods have been actively developed that restore an image, based on a statistic theory called Bayes Theory. Non-patent Document 1 discloses a manner of restore processing of an image, the manner using a quasi-Newton method with assumption that the original clear image of a blurred image is one that minimizes the cost function calculated based on Bayes Theory. Non-patent Document 2 discloses a manner of restoring an image in calculating the cost function, wherein the manner employs a minimization technique by the use of a Sherman-Morris matrix instead of the quasi-Newton method referred to in Non-patent Document 1, so as to restore the image in a shorter time than the manner by Non-patent Document 1.
[Non-patent Document 1] G. K. Chantas, N. P. Galatsanos, and A. C. Likas, ‘Bayesian Restoration Using a New Nonstationary Edge-Preserving Image Prior’, IEEE Transactions on Image Processing, Vol. 15, No. 10, pp. 2987-2997, 2006
[Non-patent Document 2] R. Pan and S. J. Reeves, ‘Efficient Huber-Markov Edge-Preserving Image Restoration’, IEEE Transactions on Image Processing, Vol. 15, No. 12, pp. 3728-3735, 2006.